Volume-outcome relationships for head and neck cancer surgery in a universal health care system
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: We aimed to assess whether surgeon and/or institution resection volume predicts long-term overall survival in head and neck cancer in a publicly funded healthcare system. STUDY DESIGN: Population-based retrospective cohort study. METHODS: Head and neck cancer patients in Ontario, Canada, who underwent a resection confirmed by both hospital-level and physician-level administrative data between 1993 and 2010, comprised our cohort (N = 5,720). Physician and hospital volumes were calculated based on number of cases performed in the year prior by the physician and at an institution performing each case, respectively. A multilevel hierarchical Cox regression model was used to estimate the effect on overall survival of each 25 increase in procedure volume. RESULTS: A crude model without patient or treatment characteristics demonstrated that both surgeon volume (hazard ratio [HR]: 0.927, 95% confidence interval [CI]: 0.879-0.978, P = .006) and hospital volume (HR: 0.980, 95% CI: 0.970-0.991, P = .0003) were associated with improved overall survival. After controlling for clustering and patient/treatment covariates, hospital volume (HR: 0.976, 95% CI: 0.955-0.997, P = .02), but not physician volume (HR: 1.042, 95% CI: 0.941-1.155, P = .43), remained a statistically significant predictor of overall survival. This translates into a 2.4% decrease in the HR for every 25 additional cases performed at an institution. CONCLUSIONS: Both high-volume surgeons and hospitals are predictors of better overall survival in head and neck cancer patients. However, the effect is largely explained by hospital volume. This benefit, at the institution level, could potentially be explained by important processes of care that contribute to overall survival.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".